Differential phospho analysis - CRC Batch 2 Set 4
Sophie Herbst
Analysis date: 2023-08-08
Depends on
CRC_Xenografts_Batch2_DataProcessing Script
load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")TODO
Setup
Load libraries and functions
Analysis
DEP
Tyrosine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set4, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set4_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: 2-LTR circle formation 0.51262136
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.45995893
## 3: ABC transporter disorders 0.73818182
## 4: ABC-family proteins mediated transport 0.73818182
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.06990291
## 6: ADP signalling through P2Y purinoceptor 1 0.73350923
## padj log2err ES NES size leadingEdge
## 1: 0.9022136 0.07627972 0.7670455 1.0078139 1 2547
## 2: 0.8669296 0.08504275 -0.7642045 -1.0260971 1 6385
## 3: 0.9295892 0.09054289 -0.3132184 -0.7678948 5 10213,5687,5692,5706,5683
## 4: 0.9295892 0.09054289 -0.3132184 -0.7678948 5 10213,5687,5692,5706,5683
## 5: 0.3917201 0.23779383 0.9659091 1.2690989 1 5575
## 6: 0.9295892 0.07362127 -0.5023155 -0.8238637 2 1432
## Note: Row-scaling applied for this heatmap
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: 2-LTR circle formation 0.76938370
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.87975952
## 3: ABC transporter disorders 0.61611374
## 4: ABC-family proteins mediated transport 0.61611374
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.01502038
## 6: ADP signalling through P2Y purinoceptor 1 0.57013575
## padj log2err ES NES size leadingEdge
## 1: 0.9611015 0.05748774 0.6079545 0.8181126 1 2547
## 2: 0.9631800 0.05163560 -0.5511364 -0.7391324 1 6385
## 3: 0.9611015 0.05712585 0.4402128 0.9030274 5 5683,5706,5692,10213
## 4: 0.9611015 0.05712585 0.4402128 0.9030274 5 5683,5706,5692,10213
## 5: 0.8534483 0.38073040 0.9971591 1.3418576 1 5575
## 6: 0.9611015 0.07871138 -0.5818796 -0.9175830 2 1432Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: 2-LTR circle formation 0.29065041
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.69785575
## 3: ABC transporter disorders 0.73214286
## 4: ABC-family proteins mediated transport 0.73214286
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.02873552
## 6: ADP signalling through P2Y purinoceptor 1 0.79639175
## padj log2err ES NES size leadingEdge
## 1: 0.9773596 0.11191832 0.8664773 1.1525733 1 2547
## 2: 0.9773596 0.06116926 -0.6761364 -0.8888176 1 6385
## 3: 0.9773596 0.08998608 -0.3247126 -0.8176925 5 5687,10213,5692,5683,5706
## 4: 0.9773596 0.08998608 -0.3247126 -0.8176925 5 5687,10213,5692,5683,5706
## 5: 0.9016803 0.35248786 0.9857955 1.3112883 1 5575
## 6: 0.9773596 0.06831109 -0.4729345 -0.7822898 2 1432,6714EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_Set4, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: 2-LTR circle formation 0.88631985
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.44969199
## 3: ABC transporter disorders 0.86685160
## 4: ABC-family proteins mediated transport 0.86685160
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.09856263
## 6: ADP signalling through P2Y purinoceptor 1 0.79545455
## padj log2err ES NES size leadingEdge
## 1: 0.9713542 0.04949049 -0.5625000 -0.7522388 1 2547
## 2: 0.8256003 0.08628656 0.7755682 1.0387136 1 6385
## 3: 0.9713542 0.03539106 -0.3547866 -0.7085520 5 5687
## 4: 0.9713542 0.03539106 -0.3547866 -0.7085520 5 5687
## 5: 0.4183188 0.20429476 0.9431818 1.2631975 1 5575
## 6: 0.9713542 0.04660151 -0.5017764 -0.7777033 2 1432## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set4, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 1 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)## pathway pval
## 1: 2-LTR circle formation 0.24302789
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.08548708
## 3: ABC transporter disorders 0.99617347
## 4: ABC-family proteins mediated transport 0.99617347
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.40954274
## 6: ADP signalling through P2Y purinoceptor 1 0.30200308
## padj log2err ES NES size leadingEdge
## 1: 0.9380927 0.12267919 0.8778409 1.1833922 1 2547
## 2: 0.8933684 0.21654284 -0.9545455 -1.2828719 1 6385
## 3: 1.0000000 0.02421535 0.2075016 0.4366359 5 5706,10213,5692,5687
## 4: 1.0000000 0.02421535 0.2075016 0.4366359 5 5706,10213,5692,5687
## 5: 0.9509109 0.08971047 -0.7954545 -1.0690599 1 5575
## 6: 0.9509109 0.09255289 0.7192957 1.1434051 2 1432,6714
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Serine/Threonine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pST <- test_diff(pST_se_Set4, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pST <- add_rejections_SH(data_diff_ctrl_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pST, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set4_form, dep_ctrl_vs_E_pST, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.8850103 0.9540714
## 2: ABC-family proteins mediated transport 0.3812261 0.7508441
## 3: ADORA2B mediated anti-inflammatory cytokines production 0.2348337 0.6272866
## 4: AKT phosphorylates targets in the cytosol 0.2689938 0.6292054
## 5: ALK mutants bind TKIs 0.4537815 0.8379488
## 6: AMPK inhibits chREBP transcriptional activation activity 0.5134100 0.8782013
## log2err ES NES size leadingEdge
## 1: 0.05248276 -0.5631579 -0.7500433 1 3159
## 2: 0.09167952 0.8105263 1.0916488 1 23
## 3: 0.12384217 0.7301587 1.1690604 2 5576,5573
## 4: 0.11776579 -0.8842105 -1.1776380 1 7249
## 5: 0.08705159 0.5797872 1.0371355 3 27436,5573,4869
## 6: 0.07550153 0.7421053 0.9994966 1 51085data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.9798387 0.9964546
## 2: ABC-family proteins mediated transport 0.7096774 0.9794140
## 3: ADORA2B mediated anti-inflammatory cytokines production 0.4896074 0.8877471
## 4: AKT phosphorylates targets in the cytosol 0.2986248 0.8463915
## 5: ALK mutants bind TKIs 0.1310680 0.6564230
## 6: AMPK inhibits chREBP transcriptional activation activity 0.7762097 0.9794140
## log2err ES NES size leadingEdge
## 1: 0.04697587 0.5105263 0.6878743 1 3159
## 2: 0.06197627 0.6368421 0.8580700 1 23
## 3: 0.08809450 0.6680607 1.0398767 2 5573,5576
## 4: 0.10797236 -0.8526316 -1.1405130 1 7249
## 5: 0.19189224 0.7503226 1.3940814 3 5573,27436,4869
## 6: 0.05773085 0.6105263 0.8226125 1 51085Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.9831224 0.9924812
## 2: ABC-family proteins mediated transport 0.4943609 0.9094893
## 3: ADORA2B mediated anti-inflammatory cytokines production 0.1808279 0.7197245
## 4: AKT phosphorylates targets in the cytosol 0.2848101 0.7675439
## 5: ALK mutants bind TKIs 0.3233831 0.7675439
## 6: AMPK inhibits chREBP transcriptional activation activity 0.6466165 0.9924812
## log2err ES NES size leadingEdge
## 1: 0.04889708 -0.5105263 -0.6860710 1 3159
## 2: 0.07647671 0.7631579 1.0291523 1 23
## 3: 0.15214492 0.8042328 1.2906577 2 5576,5573
## 4: 0.11573445 -0.8578947 -1.1528822 1 7249
## 5: 0.11828753 0.6337490 1.1660308 3 27436,5573,4869
## 6: 0.06307904 0.6789474 0.9155907 1 51085EC vs E
data_diff_EC_vs_E_pST <- test_diff(pST_se_Set4, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.6859504 0.9931178
## 2: ABC-family proteins mediated transport 0.5801527 0.9931178
## 3: ADORA2B mediated anti-inflammatory cytokines production 0.6389685 0.9931178
## 4: AKT phosphorylates targets in the cytosol 0.6198347 0.9931178
## 5: ALK mutants bind TKIs 0.3229167 0.9931178
## 6: AMPK inhibits chREBP transcriptional activation activity 0.6125954 0.9931178
## log2err ES NES size leadingEdge
## 1: 0.06479434 0.6421053 0.8669413 1 3159
## 2: 0.06911985 -0.7315789 -0.9688412 1 23
## 3: 0.08528847 0.5455885 0.8705113 2 5573
## 4: 0.06977925 0.6947368 0.9380021 1 7249
## 5: 0.14290115 0.5797872 1.1132263 3 5573,27436,4869
## 6: 0.06643641 -0.7157895 -0.9479310 1 51085#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set4, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.7917526 0.9387918
## 2: ABC-family proteins mediated transport 0.2898273 0.9387918
## 3: ADORA2B mediated anti-inflammatory cytokines production 0.7563353 0.9387918
## 4: AKT phosphorylates targets in the cytosol 0.7298969 0.9387918
## 5: ALK mutants bind TKIs 0.2940000 0.9387918
## 6: AMPK inhibits chREBP transcriptional activation activity 0.5297505 0.9387918
## log2err ES NES size leadingEdge
## 1: 0.05785298 -0.6052632 -0.8087060 1 3159
## 2: 0.10839426 0.8684211 1.1542653 1 23
## 3: 0.05736674 -0.5438133 -0.8248791 2 5573
## 4: 0.06170541 -0.6368421 -0.8508994 1 7249
## 5: 0.11012226 -0.6967521 -1.1798549 3 5573
## 6: 0.07399014 0.7421053 0.9863721 1 51085
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Session Info
sessionInfo()## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.2 forcats_1.0.0
## [3] stringr_1.5.0 dplyr_1.1.2
## [5] purrr_1.0.1 readr_2.1.4
## [7] tidyr_1.3.0 tibble_3.2.1
## [9] ggplot2_3.4.2 tidyverse_2.0.0
## [11] mdatools_0.14.0 SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [15] MatrixGenerics_1.10.0 matrixStats_1.0.0
## [17] DEP_1.20.0 org.Hs.eg.db_3.16.0
## [19] AnnotationDbi_1.60.2 IRanges_2.32.0
## [21] S4Vectors_0.36.2 Biobase_2.58.0
## [23] BiocGenerics_0.44.0 fgsea_1.24.0
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.15 fastmatch_1.1-3 plyr_1.8.8
## [4] igraph_1.5.0.1 gmm_1.8 lazyeval_0.2.2
## [7] shinydashboard_0.7.2 crosstalk_1.2.0 BiocParallel_1.32.6
## [10] digest_0.6.33 foreach_1.5.2 htmltools_0.5.5
## [13] fansi_1.0.4 magrittr_2.0.3 memoise_2.0.1
## [16] cluster_2.1.4 doParallel_1.0.17 tzdb_0.4.0
## [19] limma_3.54.2 ComplexHeatmap_2.14.0 Biostrings_2.66.0
## [22] imputeLCMD_2.1 sandwich_3.0-2 timechange_0.2.0
## [25] colorspace_2.1-0 blob_1.2.4 xfun_0.39
## [28] crayon_1.5.2 RCurl_1.98-1.12 jsonlite_1.8.7
## [31] impute_1.72.3 zoo_1.8-12 iterators_1.0.14
## [34] glue_1.6.2 hash_2.2.6.2 gtable_0.3.3
## [37] zlibbioc_1.44.0 XVector_0.38.0 GetoptLong_1.0.5
## [40] DelayedArray_0.24.0 shape_1.4.6 scales_1.2.1
## [43] pheatmap_1.0.12 vsn_3.66.0 mvtnorm_1.2-2
## [46] DBI_1.1.3 Rcpp_1.0.11 plotrix_3.8-2
## [49] mzR_2.32.0 viridisLite_0.4.2 xtable_1.8-4
## [52] clue_0.3-64 reactome.db_1.82.0 bit_4.0.5
## [55] preprocessCore_1.60.2 sqldf_0.4-11 MsCoreUtils_1.10.0
## [58] DT_0.28 htmlwidgets_1.6.2 httr_1.4.6
## [61] gplots_3.1.3 RColorBrewer_1.1-3 ellipsis_0.3.2
## [64] farver_2.1.1 pkgconfig_2.0.3 XML_3.99-0.14
## [67] sass_0.4.7 utf8_1.2.3 STRINGdb_2.10.1
## [70] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.1
## [73] later_1.3.1 munsell_0.5.0 tools_4.2.3
## [76] cachem_1.0.8 cli_3.6.1 gsubfn_0.7
## [79] generics_0.1.3 RSQLite_2.3.1 fdrtool_1.2.17
## [82] evaluate_0.21 fastmap_1.1.1 mzID_1.36.0
## [85] yaml_2.3.7 knitr_1.43 bit64_4.0.5
## [88] caTools_1.18.2 KEGGREST_1.38.0 ncdf4_1.21
## [91] mime_0.12 compiler_4.2.3 rstudioapi_0.15.0
## [94] plotly_4.10.2 png_0.1-8 affyio_1.68.0
## [97] stringi_1.7.12 bslib_0.5.0 highr_0.10
## [100] MSnbase_2.24.2 lattice_0.21-8 ProtGenerics_1.30.0
## [103] Matrix_1.6-0 tmvtnorm_1.5 vctrs_0.6.3
## [106] pillar_1.9.0 norm_1.0-11.1 lifecycle_1.0.3
## [109] BiocManager_1.30.21.1 jquerylib_0.1.4 MALDIquant_1.22.1
## [112] GlobalOptions_0.1.2 data.table_1.14.8 cowplot_1.1.1
## [115] bitops_1.0-7 httpuv_1.6.11 R6_2.5.1
## [118] pcaMethods_1.90.0 affy_1.76.0 promises_1.2.0.1
## [121] KernSmooth_2.23-22 codetools_0.2-19 MASS_7.3-60
## [124] gtools_3.9.4 assertthat_0.2.1 chron_2.3-61
## [127] proto_1.0.0 rjson_0.2.21 withr_2.5.0
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3 hms_1.1.3
## [133] grid_4.2.3 rmarkdown_2.23 shiny_1.7.4.1knitr::knit_exit()